Virtual mitochondria : metabolic modelling and control.

Transcription

1 Virtual mitochondria : metabolic modelling and control. Marie Aimar-Beurton 1,, Bernard Korzeniewski 3, Thierry Letellier 1, Stéphane Ludinard 1, Jean-erre Mazat 1 and Christine Nazaret (alphabetical order) 1 Inserm EMI, and ESTBB, Université de Bordeaux, 146 rue Léo-Saignat, F 33076, Bordeaux-cedex France and M.A.B. Université de Bordeaux 1. 3 Jagiellonian University, ul. Gronostajowa 7, Krakow, Poland Abstract Inside the eukaryotic cell, mitochondria are internal organelles of prokaryotic origin, responsible for energy supply in the cell. The control of the mitochondrial ATP production is a complex problem with different patterns according to different tissues and organs. Our aim is to continue to develop the modelling of oxidative phosphorylation in different tissues, to model other parts of mitochondrial metabolism and to include this virtual mitochondria in a virtual cell. In constructing the complete metabolic map of mitochondria, we will take advantage of the sequenced genomes of eukaryotic organism (-15% of the yeast genome concerns mitochondria). Introduction Mitochondria are internal organelles inside the eukaryotic cell; it is the place of oxidative phosphorylation (OXPHOS), i.e. of the ATP synthesis coupled to respiratory chain functioning (Fig. 1 in Appendix). Mitochondria play an important role not only in ATP synthesis but also (non-exhaustive list) in some specific metabolic pathways, in cell oxidoreduction ratio upholding, in cell calcium homeostasis and signalling, in apoptosis, etc. The mitochondrial metabolism is thus rich and varied and it is one of the aims of our work to understand how this metabolism can account for very different functions and behaviour of mitochondria in different tissues (Rossignol et al., 1; Rossignol et al., 000). The first aim of our work is to continue the modelling of oxidative phosphorylation in different tissues in order to simulate their functioning and to understand the basis in their control differences. In addition mitochondria hold a significant part of cellular metabolism: Krebs cycle, b- oxidation of fatty acids, etc., and the second aim of our work will be to model this metabolism. In the third step we will include this virtual mitochondria in a virtual cell by modelling the exchanges of metabolites, energy and signals (calcium signals) between the cell and mitochondria. In constructing the complete metabolic map of mitochondria, we will take advantage of the sequenced genomes of eukaryotic organism, from which a significant part (-15% in yeast) concerns mitochondria. Our project will also lean on sequenced genomes of prokaryotic organisms, which are ancestors of mitochondria. This should help to ascribe functions to unknown ORF, possibly involved in mitochondrial metabolism. To sum it up, our work will consist in linking sequenced genomes to mitochondrial metabolism, in order to construct mitochondrial metabolic maps and to analyse the mitochondrial fluxes and their regulation. Results Oxidative phosphorylation is probably the mitochondrial metabolic pathway that was most frequently modelled in the quantitative way. The general scheme of oxidative phosphorylation is presented in Fig. 1 in Appendix and a detailed metabolic map in Fig. of

2 the Appendix. Several different kinetic (and thermodynamic) models of this process have been developed. They are shortly summarised in Table 1 (see Appendix). Among these models, only the model developed by Korzeniewski and co-workers has been tested for a broad range of experimentally-measured parameter values and system properties. This model was used to predict new properties of the system and the existence of new phenomena. One of the most important predictions was that only a direct activation of (almost) all oxidative phosphorylation steps, in parallel with the activation of ATP usage, by some (still unknown) cytosolic factor X, transmitting the signal of stimulation of a cell by neural excitation (skeletal muscle, heart) or hormones (liver) can explain the large changes in fluxes (respiration, ATP turnover) accompanied by only moderate changes in metabolite concentrations (ATP/ADP, H/ ) in intact tissues (Korzeniewski, & Froncisz, 1; Korzeniewski, 1; Korzeniewski & Zoladz, 001). Some other important predictions concern the effect of inborn enzyme deficiencies and the ethiology of mitochondrial diseases (Korzeniewski et al., 001, Korzeniewski, 00). Computer models of other metabolic pathways located in mitochondria, for example of Krebs cycle, were also developed. These models also helped significantly to understand the control and regulation of mitochondrial metabolism. Some of the results obtained experimentally or predicted with the help of a model are understandable in the framework of metabolic control analysis (Kacser & Burns, 173; Heinrich & Rapoport, 174; Reder, 1). The values of control coefficients are of great interest in the prediction of the effects of a deficiency in mitochondrial diseases; they are also of interest in biotechnology, where some steps are amplified. The theoretical models of oxidative phosphorylation developed so far, allow more or less easily to calculate its control coefficients (Korzeniewski, & Froncisz, 1; Korzeniewski, 16a; Korzeniewski & Mazat, 16c; Korzeniewski & Mazat, 16d). Conclusion : With the help of models of mitochondrial metabolism it is possible to analyse and to compare the metabolic organisation and functioning of different types of mitochondria. The basic knowledge (based on already studied enzymes and on reasonable hypotheses) of the kinetic parameters of enzymes or enzymatic complexes will enable us to predict the metabolic fluxes, their regulation and their control. In a sense our aim is to apply to mitochondria the method developed for whole cells in the post-genomic area, i.e. to construct and to analyse the metabolic maps from the genes. Due to the lower number of genes involved in mitochondria (% of an eukaryotic genome, see table ) this application could be easier than for whole cells and is, in any case, a compulsory step in cell metabolism modelling, because mitochondria are largely autonomous and independent units inside cells. This will impose to precisely recognise these sequences, involved in mitochondria. Acknowledgements This work was supported by the Association Française contre les Myopathies (A.F.M.), INSERM, the Université Bordeaux II and the Région Aquitaine. B.K. was supported by the KBN grant 0450/P04/001/0. References Aliev, M.K., Saks, V.A., Compartmentalized energy transfer in cardiomyocytes: use of methematical modelling for analysis in vivo regulation of respiration, Biophys. J. 73 (17) Bohnensack, R., Control of energy transformation of mitochondria. Analysis by a quantitative model, Biochim. Biophys. Acta 634 (11) Bohnensack, R., Küster, U., Letko, G., Rate-controlling steps of oxidative phosphorylation in rat liver mitochondria. A synoptic approach of model and experiment, Biochim. Biophys. Acta 60 (1) 71-0.

3 Chance, B., Williams, G.R., Respiration enzymes in oxidative phosphorylation. 1. Kinetics of oxygen utilization, J. Biol. Chem. 17 (155) Chance, B., Williams, G.R., The respiratory chain and oxidative phosphorylation, Adv. Enzymol. 17 (156) Gellerich, F.N., Bohnensack, R., Kunz, W., Control of mitochondrial respiration. The contribution of the adenine nucleotide translocator depends on the ATP- and ADP-consuming enzymes, Biochim. Biophys. Acta 7 (13) Heinrich, R., Rapoport, T.A., A linear steady-state treatment of enzymatic chains. General properties, control and effector strength, Eur. J. Biochem. 4 (174), -5. Holzhütter, H.-G., Henke, W., Dubiel, W., Gerber, G., A mathematical model to study short-term regulation of mitochondrial energy transduction, Biochim. Biophys. Acta (15) 5-6. Kacser, H., Burns, J.A., The control of flux, Symp. Soc. Exp. Biol. 3 (173) Korzeniewski, B., Simulation of oxidative phosphorylation in hepatocytes, Biophys. Chem. 5 (16a) Korzeniewski, B., Simulation of state 4 Æ state 3 transition in isolated mitochondria, Biophys. Chem. 57 (16b) Korzeniewski, B., Regulation of ATP supply during muscle contraction: theoretical studies, Biochem. J. 330 (1) Korzeniewski, B., Parallel activation in the ATP supply-demand system lessens the effect of enzyme deficiencies, inhibitors, poisons and substrate shortage on oxidative phosphorylation, Biophys. Chem. 6 (00) Korzeniewski, B., Froncisz, W., An extended dynamic model of oxidative phosphorylation, Biochim. Biophys. Acta 60 (11) -3. Korzeniewski, B., Froncisz, W., Theoretical studies on the control of the oxidative phosphorylation system, Biochim. Biophys. Acta 1 (1) Korzeniewski, B., Mazat, J.-P., Theoretical studies of the control of oxidative phosphorylation in muscle mitochondria: application to mitochondrial deficiencies, Biochem. J. 31 (16a) Korzeniewski, B., Mazat, J.-P., Theoretical studies on control of oxidative phosphorylation in muscle mitochondria at different energy demands and oxygen concentrations, Acta Biotheoretica 44 (16b) Korzeniewski, B., Malgat, M., Letellier, T. and Mazat, J.-P., Effect of binary mitochondria heteroplasmy on respiration and ATP synthesis: implications to mitochondrial diseases, Biochem. J. 357 (001) Korzeniewski, B., Zoladz, J.A., A model of oxidative phosphorylation in mammalian skeletal muscle, Biophys. Chem. (001) Reder, C., Metabolic control theory: a structural approach, J. Theor. Biol. 135 (1) Rossignol, R., Malgat, M., Mazat, J.-P., Letellier, T., Threshold effect and tissue specificity. Implication for mitochondrial cytopathies, J. Biol. Chem. 74 (1) Rossignol, R., Letellier, T., Malgat, M., Rocher, C., Mazat, J.-P., Tissue variation in the control of oxidative phosphorylation: Implication for mitochondrial diseases, Biochem. J. 347 (000) Rottenberg, H., Non-equilibrium thermodynamics of energy conversion in bioenergetics, Biochim. Biophys. Acta 54 (17) Vendelin, M, Kongas, O., Saks, V., Regulation of mitochondrial respiration in heart cells analyzed by reaction-diffusion model of energy transfer, Am. J. Physiol. 7 (000) C747-C764. Westerhoff, H.V., van Dam, K., Thermodynamics and control of free-energy transduction, Elsevier, Amsterdam, 17. Wilson, D.F., Owen, C.S, Ereci_ska, M., Quantitative dependence of mitochondrial oxidative phosphorylation on oxygen concentration: a mathematical model, Arch. Biochem. Biophys. 15 (17) Wilson, D.F., Owen, C.S. and Holian, A., Control of mitochondrial respiration: a quantitative evaluation of the roles of cytochrome c and oxygen, Arch. Biochem. Biophys. 1 (177)

4 APPENDIX : Table 1. Quantitative models of oxidative phosphorylation available in the literature. Authors type of model chatacteristics references Chance and Williams one simple kinetic equation Rottenberg; Westerhoff and van Dam Wilson, Erecinska and co-workers Bohnensack and coworkers Holzhütter and coworkers Korzeniewski and coworkers Saks and co-workers NET non-equilibrium thermodynamics static kinetic model of one rate-limiting step static kinetic model dynamic kinetic model dynamic kinetic model dynamic kinetic model for isolated mitochondria; Michaelis- Menten kinetic dependence of the respiration rate on [ADP]; black-box description for isolated mitochondria; linear depencence of fluxes on thermodynamic forces; black-box description; limited range of application for isolated mitochondria; kinetic description of cytochrome oxidase assumed to be the only rate-limiting step; depencence on external [ATP]/[ADP] instead of on Dp for isolated mitochondria; kinetic description of many (but not all) complexes (phosphate carrier not included explicitly, respiratory chain described as one unit); tested for a limited set of system properties for isolated mitochondria; kinetic description of all complexes; several assumptions are not justified; developed for mitochondria working in nonphysiological temperature ( C); tested for a limited set of system properties for isolated mitochondria and intact tissues (liver, muscle); kinetic description of all complexes; tested for a broad set of system properties; used for a series of new theoretical predictions for intact heart; creatine kinase assumed to be essentially displaced from thermodynamic equilibrium; P i assumed to be the main metabolite regulating oxidative phosphorylation; contradicts several experimental data concerning the value of, and relative changes in, [P i ] Chance & Williams, 155; Chance and Williams, 16 Rottenberg; 17; Westerhoff & van Dam, 17 Wilson, et al., 177; Wilson et al., 17. Bohnensack, 11; Bohnensack et al., 1; Gellerich et al., 13. Holzhütter et al., 15. Korzeniewski & Froncisz, 11, 1; Korzeniewski, 1; Korzeniewski et al., 001; Korzeniewski, 00; Korzeniewski, 16a, 16b; Korzeniewski & Mazat, 16a, 16b ; Korzeniewski & Zoladz, 001. Aliev & Saks, 17; Vendelin et al., 000.

5 Table. Number of nuclear genes known for coding mitochondrial proteins : SwissProt was used as a data bank. The interrogation was built with two kinds of keywords: different organism names (listed in the first column) and the prefix "mito"; the number of these occurrences is listed in the third column with the number of mtdna uncoded proteins in brackets. The fourth column gives the percentage of the number of mito occurences scaled by the total number of known proteins in the organisms under study (second column). Organism Nb of Nb of mito citations % known proteins Arabidopsis thaliana (17) 6.67 Sacharomyces cerevisiae (1) 1.3 Drosophila melanogaster (13) 7.55 Caenorhabditis elegans (1) 5.05 Mus musculus (16) 7.3 Homo sapiens (13).13 Figure 1. Scheme of oxidative phosphorylation in mitochondria. SH, respiratory substrate; 1., substrate dehydrogenation;., complex I; 3., complex III; 4., complex IV; 5., proton leak; 6., ATP synthase, 7. ATP/ADP carrier;. phosphate carrier;., ATP usage.

6 H Pyruvate AMP P Aspartate ATP Citrate Fumarate Arginino succinate Arginine Urea H O 3 1 Citrulline Ornithine nh 3GP DHAP H n H S G3-PDH 30 FAMN FeS UQ b565, b566 FeS ; C1 3 Cyt c C III Cu1 ; Cu a ; a3 33 Q C IV F0 C I DHODH 34 FAD F1 H FADH 31 C II Dihydrorotate Orotate 1/ O H H O 4-4- ATP ATP ADP 3-35 ADP 3- H TP H n H Fumarate H n H S ATP CO ADP H H O FADH FAD Pyruvate Oxaloacetate Fumarate H O H 7 Acetoacetyl-CoA H O _-OH Butyrate TPr 4 5 HO methyl Glutaryl CoA 6 1 Acetoacetate H CO _-OH Butyrate H CO Succinyl-CoA Oxaloacetate Aspartate 7 GDP GTP Methylmalonyl-CoA H H O AMP P ATP CO Propionyl-CoA At the end of the loop, if nc is not par TT 3 Citrate 4 Isocitrate H CO _-Ketoglutarate 6 5 Citrulline ADP ATP CO 1 Carnitine H 36 0 Carbamoyl-P 1 NH 3 Ornithine Glutamate Cplx TIM Cplx TIM3 _-Ketoglutarate Cplx OXA1 Cplx H TOM Acyl-CoA (n-c) - 15 _-cetoacyl-coa H 14 _-Ketoglutarate TCg _-OH-acyl-CoA H H O 13 Glutamate TGA Trans enoyl CoA Aspartate FADH 1 Ca Na FAD E Acyl-CoA (Cn) or H Ca CCVD - ATP E E TG Made by Rachid OUHABI & Stéphane LUDINARD Carnitine 11 Carnitine Acyl-CoA (>1C) Acyl-CoA (<1C) Figure. Mitochondrial metabolism.